Cluster Computing

, Volume 22, Supplement 6, pp 13337–13350 | Cite as

Research on data mining of permissions mode for Android malware detection

  • Chao Wang
  • Qingzhen XuEmail author
  • Xiuli Lin
  • Shouqiang Liu


Android system uses a permission mechanism to allow users and developers to regulate access to private information and system resources required by Android applications (apps). Permissions can be behaviors and characteristics of an app, and widely used by Android malware detection. This paper designs a novel method to extract contrasting permission patterns for comparing the differences between Android benign apps and malware based on permissions, and use these differences to detect Android malware. Unlike existing works, this work first analyzes required and used permission. Then use support-based permission candidate method to mining unique required or used permission patterns, and use these patterns to detect Android malware. In experiment, this approach uses permission patterns from Android malware to detect a mixed Android app dataset. The results show that the proposed method can achieve 94% accuracy, 5% false positive, and 1% false negative.


Android required permission Android used permission Malware detection Permission pattern Contrasting mining 



The Project was supported by the National Natural Science Foundation of China (No. 61402185), Science Foundation of Guangdong Provincial Communications Department (grant number 2015-02-064), Natural Science Foundation of Guangdong Province (No. 2015A030313382), Guangdong Provincial Public Research and Capacity Building Foundation funded project (No. 2015A020217011 & 2016A020223012), STPF of University in Shandong Province of China (J17KA161), and South China Normal University–Bluedon Information Security Technologies Co., Ltd joint laboratory project LD20170201.


  1. 1.
    Zhou, J., Dong, H., Feng, J.: Event-triggered communication for synchronization of Markovian jump delayed complex networks with partially unknown transition rates. Appl. Math. Comput. 293(C), 617–629 (2017)MathSciNetzbMATHGoogle Scholar
  2. 2.
    Gilbert, P., Byung-Gon, C., Landon, P.C., Jaeyeon, J.: Vision: automated security validation of mobile apps at app markets. 2001 International Workshop on Mobile Cloud Computing and Services, pp. 21–26. ACM, New York (2011)Google Scholar
  3. 3.
    Frank, M., Dong, B., Felt, A.P., Song, D.: Mining permission request patterns from Android and Facebook applications. 2012 IEEE International Conference on Data Mining, pp. 870–875. IEEE Computer Society, Washington (2012)Google Scholar
  4. 4.
    Felt, A.P., Ha, E., Egelman, S., Haney, A., Chin, E., Wagner, D.: Android permissions: user attention, comprehension and behavior. 2012 Symposium on Usable Privacy and Security, pp. 1–14. ACM, New York (2012)Google Scholar
  5. 5.
    Zhou, Y., Jiang, X.: Dissecting, Android malware: characterization and evolution. 2012 IEEE Symposium on Security and Privacy, pp. 95–109. IEEE Computer Society, Washington (2012)Google Scholar
  6. 6.
    Tchakounte, F.: Permission-based malware detection mechanisms on android: analysis and perspectives. J. Comput. Sci. Softw. Appl. 1(2), 63–77 (2014)Google Scholar
  7. 7.
    Liang, S., Du, X.: Permission-combination-based scheme for Android mobile malware detection. ICC 2014: IEEE International Conference on Communications Washington, pp. 2301-2306. IEEE Computer Society (2014)Google Scholar
  8. 8.
    Feldman, S., Stadther, D., Wang, B.: Manilyzer: automated android malware detection through manifest analysis. MASS 2014: IEEE 11th International Conference on Mobile Ad Hoc and Sensor Systems, pp. 767–772. IEEE Computer Society, Washington (2014)Google Scholar
  9. 9.
    Rovelli, P., Vigfusson, Y.: PMDS: permission-based malware detection system. Inf. Syst. Security Lect. Notes Comput. Sci. 8880, 338–357 (2014)Google Scholar
  10. 10.
    Gong, H., Li, J., Xu, C.: Local well-posedness of strong solutions to density-dependent liquid crystal system. Nonlinear Anal. Theory Methods Appl. 147, 26–44 (2016)MathSciNetzbMATHGoogle Scholar
  11. 11.
    Li, X., Jingna, L., Qiang, L.: Existence of weak solutions for some singular parabolic equation. Acta Math. Scientia 36(6), 1651–1661 (2016)MathSciNetzbMATHGoogle Scholar
  12. 12.
    Zhang, J., Feng, Z., Xu, P., Liang, H.: Generalized varying coefficient partially linear measurement errors models. Ann. Inst. Stat. Math. 69(1), 97–120 (2017)MathSciNetzbMATHGoogle Scholar
  13. 13.
    Kong, D., Li, W., Zou, Y.: On small bases which admit points with two expansions. J. Number Theory 173, 100–128 (2017)MathSciNetzbMATHGoogle Scholar
  14. 14.
    Arp, D., Spreitzenbarth, M., Hübner, M., Gascon, H., Drebin, R.K.: Effective and explainable detection of Android malware in your pocket. Network and Distributed System Security Symposium, pp. 199–210. IEEE Computer Society, Washington (2014)Google Scholar
  15. 15.
    Yao, H., Xiong, M., et al.: Mining multiple spatial–temporal paths from social media data. Future Generation Computer Systems, onlineGoogle Scholar
  16. 16.
    Liu, S., Young, S.D.: A survey of social media data analysis for physical activity surveillance. J. Forensic Legal Med., OnlineGoogle Scholar
  17. 17.
    Blazquez, D., Domenech, J.: Big Data sources and methods for social and economic analyses. OnlineGoogle Scholar
  18. 18.
    Injadat, M.N., Salo, F., Nassif, A.B.: Data mining techniques in social media: a survey. Neurocomputing 214, 654–670 (2016)Google Scholar
  19. 19.
    Shao, H., Zhang, Y., Li, W.: Extraction and analysis of city’s tourism districts based on social media data. Comput. Environ. Urban Syst. 65, 66–78 (2017)Google Scholar
  20. 20.
    Brandt, T., Bendler, J., Neumann, D.: Social media analytics and value creation in urban smart tourism ecosystems. Inf. Manag. 54, 703–713 (2017)Google Scholar
  21. 21.
    Cui, W., Wang, P.: An algorithm for event detection based on social media data. Neurocomputing 254, 53–58 (2017)Google Scholar
  22. 22.
    Singh, A., Shukl, N., et al.: etc. Social media data analytics to improve supply chain management in food industries. Transp. Res. Part E. onlineGoogle Scholar
  23. 23.
    Qiang, W., Zhi, J., Yan, X.: Service discovery for internet of things based on probabilistic topic model. J. Softw. 25(8), 1640–1658 (2014)Google Scholar
  24. 24.
    Zhihong, Q., Yiju, W.: loT technology and application. ACTA Electronica Sinica 40(5), 1023–1029 (2012)Google Scholar
  25. 25.
    Yunquan, G., Xiaoyong, L., Binxing, F.: Survey on the search of Internet of things. J. Commun. 36(12), 57–76 (2015)Google Scholar
  26. 26.
    Haiming, C., Li, C., Kaibin, X.: A comparative study on architectures and implementation methodologies of internet of things. Chin. J. Comput. 36(1), 168–188 (2013)Google Scholar
  27. 27.
    Haiming, C., Li, C., Kaibin, X.: Information sensing and interaction technology in internet of things. Chin. J. Comput. 35(6), 1147–1163 (2012)Google Scholar
  28. 28.
    Qinyan, M., Shubin, S.: Information model and capability analysis of Internet of things. J. Softw. 25(8), 1685–1695 (2014)Google Scholar
  29. 29.
    l-Fuqaha, A., Guizani, M., et al.: Internet of things: a survey on enabling technologies, protocols and applications. IEEE Commun. Surv. Tutor. (2015)Google Scholar
  30. 30.
    Nan, J., Liang, Y., et al.: A novel exercise thermophysiology comfort prediction model with fuzzy logic. Mob. Inf. Syst. (2016)Google Scholar
  31. 31.
    Qingzhen, X., Susu, B., et al.: Mx/G/1 queue with multiple vacations. Stoch. Anal. Appl. 25(1), 127–140 (2007)MathSciNetzbMATHGoogle Scholar
  32. 32.
    Bo, C., Wen-Sheng, C.: Noisy image segmentation based on wavelet transform and active contour model. Appl. Anal. 90(8), 1243–1255 (2011)MathSciNetzbMATHGoogle Scholar
  33. 33.
    Bo, C., Qing-Hua, Z., et al.: A novel adaptive partial differential equation model for image segmentation. Appl. Anal. 93(11), 2440–2450 (2012)zbMATHGoogle Scholar
  34. 34.
    Xiuli, L., Zengqin, Z.: Iterative technique for a third-order differential equation with three-point nonlinear boundary value conditions. Electron. J. Qual. Theory Differ. Equ. 12(1), 1–10 (2016). MathSciNetCrossRefzbMATHGoogle Scholar
  35. 35.
    Qingzhen, X., Zhoutao, W., et al.: Thermal comfort research on human CT data modeling. Multimed. Tool. Appl. MathSciNetGoogle Scholar
  36. 36.
    Yang, S., Hu, C.: Pure Weierstrass gaps from a quotient of the Hermitian curve. Finite Fields Appl. 50, 251–271 (2018)MathSciNetzbMATHGoogle Scholar
  37. 37.
    Peihe, W., Lingling, Z.: Some geometrical properties of convex level sets of minimal graph on 2-dimensional Riemannian manifolds. Nonlinear Anal. Theory Method Appl. 130(1), 1–13 (2016)MathSciNetzbMATHGoogle Scholar
  38. 38.
    Peihe, W., Dekai, Z.: Convexity of level sets of minimal graph on space form with nonnegative curvature. J. Differ. Equ. 262, 5534–5564 (2017)MathSciNetzbMATHGoogle Scholar
  39. 39.
    Fushan, L., Qingyong, G.: Blow-up of solution for a nonlinear Petrovsky type equation with memory. Appl. Math. Comput. 274, 383–392 (2016)MathSciNetzbMATHGoogle Scholar
  40. 40.
    Li, G., Zhang, Z., Wang, L., Pan, J., Chen, Q.: One-class collaborative filtering based on rating prediction and ranking prediction. Knowl. Based Syst. 124: 46-54 (2017)Google Scholar
  41. 41.
    Li, G., Ou, W.: Pairwise probabilistic matrix factorization for implicit feedback collaborative filtering. Neurocomputing 204, 17–25 (2016)Google Scholar
  42. 42.
    Li, G., Wang, L., Li, Y.: Robust personalized ranking from implicit feedback. Int. J. Pattern Recognit. Artif. Intell. 30(1), 1659001:1-28 (2016)Google Scholar
  43. 43.
    Li, G., Chen, Q.: Exploiting explicit and implicit feedbacks for personalized ranking. Math. Probl. Eng. 2016, 1–11 (2016)MathSciNetzbMATHGoogle Scholar
  44. 44.
    Xu, R., Meng, F.: Some new weakly singular integral inequalities and their applications to fractional differential equations. J. Inequal. Appl. 2016(1), 1–16 (2016)MathSciNetGoogle Scholar
  45. 45.
    Mao, A., Yang, L., et al.: Existence and concentration of solutions of Schroinger-Poisson system. Appl. Math. Lett. 68, 8–12 (2017)MathSciNetGoogle Scholar
  46. 46.
    Li, P., Ren, G.: Some classes of equations of discrete type with harmonic singular operator and convolution. Appl. Math. Comput. 284, 185–194 (2016)MathSciNetzbMATHGoogle Scholar
  47. 47.
    Wang, B., Iserles, A., Wu, X.: Arbitrary order trigonometric fourier collocation methods for multi-frequency oscillatory systems. Found. Comput. Math. 16(1), 151–181 (2016)MathSciNetzbMATHGoogle Scholar
  48. 48.
    Liu, G., Xu, S., et al.: New insight into reachable set estimation for uncertain singular time-delay systems. Appl. Math. Comput. 320, 769–780 (2018)MathSciNetzbMATHGoogle Scholar
  49. 49.
    Mao, A., Chang, H.: Kirchhoff type problems in RN with radial potentials and locally Lipschitz functional. Appl. Math. Lett. 62, 49–54 (2016)MathSciNetzbMATHGoogle Scholar
  50. 50.
    Chen, Z.-M., Xiong, X.: Equilibrium states of the Charney-DeVore quasi-geostrophic equation in mid-latitude atmosphere. J. Math. Anal. Appl. 444(2), 1403–1416 (2016)MathSciNetzbMATHGoogle Scholar
  51. 51.
    Wang, B., Iserles, A., Wu, X.: Arbitrary order trigonometric Fourier collocation methods for second-order ODEs. Found. Comput. Math. 16, 151–181 (2016)MathSciNetzbMATHGoogle Scholar
  52. 52.
    Wang, B., Wu, X., Meng, F.: Trigonometric collocation methods based on Lagrange basis polynomials for multi-frequency oscillatory second order differential equations. J. Comput. Appl. Math. 313, 185–201 (2017)MathSciNetzbMATHGoogle Scholar
  53. 53.
    Wang, B., Yang, H., Meng, F.: Sixth order symplectic and symmetric explicit ERKN schemes for solving multi frequency oscillatory nonlinear Hamiltonian equations. Calcolo 54, 117–140 (2017)MathSciNetzbMATHGoogle Scholar
  54. 54.
    Yang, S., Hu, C.: Weierstrass semigroups from Kummer extensions. Finite Fields Appl. 45, 264–284 (2017)MathSciNetzbMATHGoogle Scholar
  55. 55.
    Yang, S., Yao, Z.-A., Zhao, C.-A.: The weight distributions of two classes of p-ary cyclic codes with few weights. Finite Fields Appl. 44, 76–91 (2017)MathSciNetzbMATHGoogle Scholar
  56. 56.
    Yang, S., Yao, Z.-A.: Complete weight enumerators of a class of linear codes. Dis-crete Math. 340, 729–739 (2017)MathSciNetzbMATHGoogle Scholar
  57. 57.
    Ma, X., Wang, P., Wei, W.: Constant mean curvature surfaces and mean curvature flow with non-zero Neumann boundary conditions on strictly convex domains. J. Funct. Anal. 274, 252–277 (2018)MathSciNetzbMATHGoogle Scholar
  58. 58.
    Yang, J., Li, J., Liu, S.: A novel technique applied to the economic investigation of recommender system. Multimed. Tools Appl. Google Scholar
  59. 59.
    Yang, J., Li, J., Liu, S.: A new algorithm of stock data mining in Internet of multimedia things. J. Supercomput.
  60. 60.
    Zhang, Q.: Mathematical modeling and numerical study of carbonation in porous concrete materials. Appl. Math. Comput. 281, 16–27 (2016)zbMATHGoogle Scholar
  61. 61.
    Zhang, J., Chen, Q., Zhou, N.: Correlation analysis with additive distortion measurement errors. J. Stat. Comput. Simul. 87(4), 664–688 (2016)MathSciNetGoogle Scholar
  62. 62.
    Li, Y., Wang, R., Yao, N., Zhang, S.: A moderate deviation principle for stochastic Volterra equation. Stat. Probab. Lett. 122, 79–85 (2017)MathSciNetzbMATHGoogle Scholar
  63. 63.
    Tan, W., Ji, Y.: On the pullback attractor for the non-autonomous SIR equations with diffusion. J. Math. Anal. Appl. 449(2), 1850–1862 (2017)MathSciNetzbMATHGoogle Scholar
  64. 64.
    Tang, H., Wang, Y.: Quantitative versions of the joint distributions of Hecke eigenvalues. J. Number Theory 169, 295–314 (2016)MathSciNetzbMATHGoogle Scholar
  65. 65.
    Peng, X., Shang, Y., Zheng, X.: Lower bounds for the blow-up time to a nonlinear viscoelastic wave equation with strong damping. Appl. Math. Lett. 76, 66–73 (2018)MathSciNetzbMATHGoogle Scholar
  66. 66.
    Li, F., Li, J.: Global existence and blow-up phenomena for p-Laplacian heat equation with inhomogeneous Neumann boundary conditions. Bound. Value Probl. 2014, 219 (2014)MathSciNetzbMATHGoogle Scholar
  67. 67.
    Li, F., Li, J.: Global existence and blow-up phenomena for nonlinear divergence form parabolic equations with inhomogeneous Neumann boundary conditions. J. Math. Anal. Appl. 385, 1005–1014 (2012)MathSciNetzbMATHGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Chao Wang
    • 1
  • Qingzhen Xu
    • 1
    Email author
  • Xiuli Lin
    • 2
  • Shouqiang Liu
    • 3
  1. 1.Department of Information EngineeringGuangzhou Huashang vocational collegeGuangzhouChina
  2. 2.School of Mathematical SciencesQufu Normal UniversityQufuChina
  3. 3.School of Physics and Telecommunications EngineeringSouth China Normal UniversityGuangzhouChina

Personalised recommendations